Collabortive Research: DDDAS-TMRP: MIPS: A Real-Time Measurement-Inversion-Prediction-Steering Framework for Hazardous Events

合作研究：DDDAS-TMRP：MIPS：危险事件实时测量-反演-预测-引导框架

• 批准号: 0540372
• 负责人: Omar Ghattas
• 金额: --
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540372&HistoricalAwards=false
• 关键词: Collabortive; Research; DDDAS; TMRP; MIPS

The project will develop a multiscale, data-driven, high performance computational framework for real-time reconstruction of hazardous events from sparse measurements, and consequent probabilistic prediction of the evolution of the hazard. The framework is distinguished by four phases that are performed continually with dynamically-obtained data over the lifetime of the hazardous event. (1) Measurement: Distributed sensors provide dynamic measurements over a specified time horizon that will be used to reconstruct the initial conditions of the event. (2) Inversion: Driven by the sparse measurements, an inverse problem is solved to estimate the initial conditions for the equations governing the evolution of the hazard. (3) Prediction: Statistical analysis of the inversion results permits estimation of the uncertainty in the initial conditions, which is propagated into a prediction of the evolution of the hazard and its uncertainty. (4) Steering: Sensors are steered to new locations based on an effectivity index that incorporates sensitivities of the inversion with respect to sensor location, estimated uncertainty in the prediction, and population density factors. Continual application of the measure-invert-predict-steer (MIPS) framework described above results in updated predictions of the evolving hazard with built-in uncertainty estimates, as well as revised sensor deployment strategies that refine the predictions to reduce their uncertainty. The methods developed consider two time scales of decision making at which the MIPS framework must execute. The seconds-to-minutes decision-making scale is required by first responders to begin immediate response efforts. For such time scales, high-fidelity models in the form of partial differential equations (PDEs) are too formidable. Instead, the proposed methods will construct reduced-order models of the PDEs to facilitate realtime execution of the MIPS framework. The minutes-to-hours decision-making scale permits more careful and measured response by emergency officials using high-fidelity, high-resolution PDE models. To enable rapid execution of the MIPS cycle for such models, the project will develop fast, scalable, parallel algorithms for inversion and prediction. To demonstrate, assess, harden, robustify, and the resulting framework, will be validated on a specific application testbed: prediction of the urban/regional dispersion of intentionally- or accidentally-released atmospheric contaminants from sparse measurements.

该项目将开发一个多尺度、数据驱动、高性能的计算框架，用于从稀疏测量中实时重建危险事件，并随后对危险演变进行概率预测。该框架由四个阶段区分开来，这些阶段在危险事件的整个生命周期内使用动态获得的数据连续执行。(1)测量：分布式传感器在指定的时间范围内提供动态测量，用于重建事件的初始条件。(2)反演：在稀疏测量数据的驱动下，求解危害演化方程初始条件的反演问题。(3)预测：对反演结果进行统计分析，可以估计出初始条件下的不确定性，并将其传播为对灾害及其不确定性演变的预测。(4)转向：传感器被引导到新的位置，基于一个效率指标，该指标结合了相对于传感器位置的反演灵敏度、预测中估计的不确定性和人口密度因素。持续应用上述测量-反演-预测-导向（MIPS）框架，可以更新对不断变化的危险的预测，并内置不确定性估计，以及修改传感器部署策略，以改进预测以减少不确定性。所开发的方法考虑了MIPS框架必须执行的两个决策时间尺度。第一响应者需要秒到分钟的决策尺度来开始立即的反应工作。对于这样的时间尺度，以偏微分方程（PDEs）形式的高保真模型过于强大。相反，所提出的方法将构建pde的降阶模型，以促进MIPS框架的实时执行。几分钟到几小时的决策尺度允许应急官员使用高保真度、高分辨率的PDE模型做出更谨慎和有分寸的反应。为了使这些模型能够快速执行MIPS周期，该项目将开发快速、可扩展、并行的反演和预测算法。为了演示、评估、强化、巩固和最终的框架，将在一个特定的应用试验台上进行验证：预测稀疏测量中有意或无意释放的大气污染物在城市/区域的扩散。